Coke oven excess air coefficient control method and system based on real-time monitoring

Abstract

The invention relates to the technical field of monitoring control, in particular to a coke oven excess air coefficient control method and system based on real-time monitoring, and the method comprises the steps: collecting coke oven equipment data and thermal stress distribution through a sensor, obtaining time-varying sequence data of an excess air coefficient, carrying out frequency domain analysis, and determining a periodic model of the excess air coefficient; based on the periodic model and the real-time data, a future air excess coefficient sequence is predicted, when a prediction result is not in a reference range, the furnace body is divided into a plurality of areas according to real-time thermal stress distribution in the furnace body, the distance between each area and an air inlet and the corresponding air supply component are calculated, and the air supply rate is regulated and controlled according to the parameters. And after adjustment, thermal stress distribution and air excess coefficient sequence data are obtained again, whether air supply meets the requirement or not is judged, and if not, a corresponding adjustment strategy is obtained. The method can dynamically optimize the air supply of the coke oven, improve the combustion stability and ensure the coke production quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of monitoring control, in particular to a coke oven air excess coefficient control method and system based on real-time monitoring. BACKGROUND

[0002] In the production process of coke oven, combustion stability is one of the key factors affecting coke quality, energy utilization efficiency and equipment life. The coke oven combustion process involves complex physical and chemical reactions, and its stability is dynamically affected by many factors, including coal gas composition fluctuation, air supply rate change, uneven temperature distribution of the oven body, and cooling water flow adjustment lag, etc. Among them, the air excess coefficient, as an important indicator to measure the combustion efficiency, directly reflects the stability state of the combustion process with the fluctuation characteristics changing over time. Similar prior art is Chinese patent CN109385285B, which proposes an automatic heating optimization system for coke oven, including a feedforward control system and a feedback control system. The feedforward control system is used to collect real-time data of coke oven production, calculate the set value of the control parameter through an energy prediction model, and send the set value to the coke oven control system to realize feedforward coarse adjustment. The feedback system includes a coking monitoring system, a straight-line temperature detection system and a waste gas oxygen content detection system, which calculates the set value of the control parameter through a fuzzy control model and sends it to the coke oven control system to realize feedback fine adjustment. The automatic heating optimization system for coke oven collects and analyzes the change amount of various condition factors of coking production, calculates the set value of coke oven heating gas flow, guides the coke oven heating control and its optimization, and realizes the automatic control of uniform heating of the coke oven.

[0003] Similar prior art is Chinese patent application CN105487379A, which proposes a prediction function control method for oxygen content of coking heating furnace. First, a large amount of data collected in the industrial process is used to model the industrial process, which is composed of linear and nonlinear parts. Then, the nonlinear part is modeled by using back propagation neural network, and the linear part is modeled by using traditional two-point method. The established model is optimized by using prediction function control method, and the control variable output at the next time is determined by feedback correction. The invention uses prediction function control instead of traditional PID control, which can more effectively improve the dynamic performance and stability of the system.

[0004] The technical solutions in the above two patent documents realize the control of coke oven production, but do not consider the dynamic balance between the periodic variation law of oxygen concentration in the combustion chamber and the thermal stress of the coke oven structure, and cannot realize the collaborative optimization of combustion stability and production quality. SUMMARY

[0005] The application provides a coke oven air excess coefficient control method based on real-time monitoring, mainly comprising:

[0006] Coke oven equipment data and thermal stress distribution are collected by sensors to obtain sequence data of air excess coefficient changing with time;

[0007] Frequency domain analysis is performed on the air excess coefficient sequence data to determine a periodic model of the air excess coefficient;

[0008] A predicted air excess coefficient sequence is obtained according to the frequency domain data corresponding to the obtained real-time air excess coefficient and the periodic model; when the predicted air excess coefficient sequence is not within a corresponding reference range, the oven body is divided into multiple regions according to the real-time thermal stress distribution in the oven body, the distance of each region from an air inlet is calculated, the air supply component corresponding to each region is calculated, and the air supply rate is regulated according to the distance and the air supply component corresponding to each region;

[0009] The thermal stress distribution and the air excess coefficient sequence data are reobtained to determine whether the air supply meets the requirements, and a corresponding adjustment strategy is obtained when the requirements are not met.

[0010] As a preferred technical solution of the application, the obtaining of the air excess coefficient sequence data changing with time comprises:

[0011] Oven body temperature distribution data, air supply rate data and coal gas flow fluctuation data are collected in real time by sensors of each device of the coke oven; the air excess coefficient sequence data changing with time is calculated based on the oven body temperature distribution data, the air supply rate data and the coal gas flow fluctuation data; the sequence data is subjected to data cleaning and outlier elimination to obtain the air excess coefficient sequence data.

[0012] As a preferred technical solution of the application, the determination of the periodic model of the air excess coefficient comprises:

[0013] The air excess coefficient sequence data is analyzed by using a fast Fourier transform algorithm to extract periodic fluctuation characteristics; the periodic change law of the oxygen concentration of the combustion chamber is analyzed according to the periodic fluctuation characteristics; the main fluctuation frequency and amplitude characteristics are determined according to the periodic change law; and the periodic model of the oxygen concentration of the combustion chamber changing with time is generated through the main fluctuation frequency and amplitude characteristics.

[0014] As a preferred technical solution of the application, the obtaining of the predicted air excess coefficient sequence comprises:

[0015] According to the frequency domain transformation result corresponding to the acquired real-time air surplus coefficient sequence data, input the periodic model, obtain a periodic mode, and obtain a predicted air surplus coefficient sequence based on the reference air surplus coefficient sequence data corresponding to the periodic mode and the real-time air surplus coefficient sequence.

[0016] As a preferred technical solution of the present application, when the predicted air surplus coefficient sequence is not within the corresponding reference range, the air supply rate regulation comprises:

[0017] The temperature sensors arranged at different positions of the furnace body acquire the thermal stress distribution in the furnace body in real time, and the regions are divided according to the thermal stress distribution. The air supply component of each region is calculated according to the temperature, the amount of coal gas and the corresponding air amount ratio of each region. The distance of each region from the air inlet is calculated, and each region is sorted according to the distance from large to small. The air flow rate is adjusted based on the sorting, the air supply component corresponding to each region and the distance. The farther the distance corresponding to the region is, the greater the air flow rate corresponding to the region is, and vice versa. When the distance is greater than a preset distance, the actual air supply component is increased by a preset proportion of the corresponding air supply component.

[0018] As a preferred technical solution of the present application, judging whether the air supply meets the requirements comprises:

[0019] After the air supply rate is adjusted, the thermal stress distribution and the air surplus sequence data are reacquired. The temperature uniformity is also acquired according to the difference between the maximum temperature value and the minimum temperature value in the thermal stress distribution. When the temperature uniformity and the air surplus coefficient sequence data are within the corresponding set range, the air supply meets the requirements, otherwise, the air supply does not meet the requirements.

[0020] As a preferred technical solution of the present application, when the air supply does not meet the requirements, the corresponding adjustment strategy is acquired, comprising:

[0021] When the air supply does not meet the requirements, and the temperature uniformity is not within the corresponding set range and the air surplus coefficient sequence data is within the corresponding range, the air supply rate corresponding to each region is adjusted according to the temperature of the adjacent region of the region in the air flow direction when the deviation value of the temperature in each region from the optimal temperature is greater than a set value. When the temperature uniformity is within the corresponding range and the air surplus coefficient sequence is not within the corresponding range, the air supply component in each region is reduced or increased in equal proportion according to the deviation of the average temperature from the optimal temperature. When the temperature uniformity and the air surplus coefficient sequence data are not within the corresponding range, the air supply rate and the previous steps are repeated.

[0022] As a preferred technical scheme of the present application, the temperature sensors in the coke oven are uniformly distributed.

[0023] The present application also provides a coke oven air excess coefficient control system based on real-time monitoring, which is used to realize the above method, and the system comprises:

[0024] An acquisition unit is configured to acquire the coke oven equipment data and the thermal stress distribution through the sensor, and acquire sequence data of the air excess coefficient changing over time.

[0025] A determination unit is configured to perform frequency domain analysis on the air excess coefficient sequence data, and determine a periodic model of the air excess coefficient.

[0026] A prediction unit is configured to acquire a predicted air excess coefficient sequence according to the frequency domain data corresponding to the real-time air excess coefficient and the periodic model.

[0027] A regulation unit is configured to, when the predicted air excess coefficient sequence is not within a corresponding reference range, divide the oven body into multiple regions according to the real-time thermal stress distribution in the oven body, calculate the distance of each region from the air inlet, calculate the air supply component corresponding to each region, regulate the air supply rate according to the distance and the air supply component corresponding to each region, reacquire and determine whether the air supply meets the requirements according to the thermal stress distribution and the air excess coefficient sequence data, and acquire a corresponding adjustment strategy when the requirements are not met.

[0028] The present application also provides a computer readable storage medium, which stores instructions, and the instructions are executed by a processor to realize the above method.

[0029] The present application has at least the following advantages:

[0030] The application collects the temperature distribution of the coke oven, the air supply rate and the coal gas flow data in real time, constructs the time sequence of the air excess coefficient, and uses frequency domain analysis (such as fast Fourier transform) to extract the periodic fluctuation rule of the oxygen concentration in the combustion chamber to establish an accurate periodic model. Based on the model, the future trend of the air excess coefficient is predicted, and the oven body is dynamically divided into multiple regions combined with the thermal stress distribution, the distance of each region from the air inlet and the required air supply component are calculated, and the partition control is realized: the air flow rate in the region far from the inlet is increased by a preset proportion to compensate for the oxygen transmission loss; the flow rate in the region close to the inlet is reduced to avoid local oxygen excess. After the control, the combustion stability is judged through the double check of the temperature uniformity, i.e. the difference between the highest temperature and the lowest temperature, and the air excess coefficient sequence: if both of them meet the standard, the parameters are maintained; if the temperature uniformity is abnormal, the air supply component is adjusted according to the regional temperature deviation; if the air excess coefficient is abnormal, the air supply amount is adjusted proportionally in the whole region; if both of them are abnormal, iterative optimization is performed. The mutual cooperation between the above technical solutions predicts the periodic fluctuation of oxygen concentration through the frequency domain model, adjusts the parameters in advance, reduces the combustion instability caused by the fluctuation of coal gas or uneven thermal stress, significantly reduces the waste gas emission fluctuation and the coke quality fluctuation, the partition control strategy combines the distance weight and the thermal stress data to effectively balance the temperature in the oven, avoids the thermal stress damage caused by local high temperature or low temperature, and takes the temperature uniformity and the air excess coefficient as the double feedback indexes to form a "prediction-control-check-iteration" closed loop, dynamically cooperates and optimizes the combustion efficiency and the production quality, and overcomes the defects that the traditional method cannot balance the periodicity of oxygen concentration and the thermal stress. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 A flow chart of a coke oven air excess coefficient control method based on real-time monitoring according to the present application.

[0032] Figure 2 A structure diagram of a coke oven air excess coefficient control system based on real-time monitoring according to the present application. DETAILED DESCRIPTION

[0033] The technical solutions of the present application will be described clearly and completely in combination with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the scope of protection of the present application.

[0034] As Figure 1 , the present embodiment is a coke oven air excess coefficient control method based on real-time monitoring, which can specifically include:

[0035] Step S1: Collecting the coke oven equipment data and the thermal stress distribution through the sensor to obtain the sequence data of the change of the air excess coefficient with time;

[0036] Specifically, a plurality of sensors in the coke oven obtain dynamic data of each device in the coke oven by real-time monitoring, including temperature sensors, air supply rate sensors, and gas flow meters, etc. The temperature sensors are uniformly distributed in each part of the oven body to obtain the internal temperature distribution of the coke oven in real time, reflecting the thermal stress state in the oven body. The air supply rate sensor is responsible for measuring the amount of air flowing into the coke oven per unit time, and the gas flow meter records the change of gas flow.

[0037] Based on these real-time data, combined with the corresponding relationship between the proportion of coke oven gas composition and the required air quantity, the air excess coefficient in the coke oven is calculated. The air excess coefficient (a) is represented by the ratio of the actual air quantity to the theoretical air quantity. The above technical solution can monitor the combustion process in the coke oven in real time, accurately obtain key data such as temperature, air flow and gas flow, and provide detailed basic data for subsequent analysis. By continuously monitoring the change of air excess coefficient, the fluctuation of combustion stability can be captured in time, providing prior information for the optimization of combustion process.

[0038] Step S2: Frequency domain analysis is performed on the air excess coefficient sequence data to determine the periodic model of the air excess coefficient;

[0039] Specifically, the air excess coefficient sequence data obtained in step S1 is analyzed by frequency domain transformation, which can convert time series data into frequency domain data, thereby extracting the periodic fluctuation characteristics of the air excess coefficient, identifying the main fluctuation frequency and amplitude characteristics in the data. These frequencies and amplitudes represent the periodic variation law of the air excess coefficient in the combustion process, reflecting the dynamic characteristics of the combustion in the coke oven, especially the change period of oxygen concentration. After obtaining the frequency domain data, by analyzing the periodic fluctuation characteristics, a periodic model describing the variation law of the air excess coefficient can be established. This model is used to accurately predict the periodic variation trend of oxygen, i.e. the air excess coefficient, in the combustion process in the coke oven. The above technical solution can eliminate random noise in time series data through frequency domain analysis, revealing the periodic fluctuation law of the air excess coefficient. This process provides a prediction model for subsequent prediction and adjustment, and the establishment of the periodic model can effectively guide the operation adjustment of the coke oven and improve the combustion stability.

[0040] Step S3: Obtain the predicted air excess coefficient sequence according to the frequency domain data corresponding to the real-time air excess coefficient and the periodic model;

[0041] Specifically, the dependence on the periodic model obtained in the previous step and the real-time collected air excess coefficient data, by transforming the real-time air excess coefficient sequence in the frequency domain, the frequency domain data is obtained; then, the frequency domain data is compared and matched with the established periodic model to predict the trend of the air excess coefficient in the future; the periodic model provides reference values for the future air excess coefficient, which represents the periodic change pattern of the oxygen concentration in the combustion process. By comparing the real-time air excess coefficient data with the reference values, the prediction model can determine the trend of the future air excess coefficient and predict whether it will exceed the set reference range; the above technical solution can predict the trend of the air excess coefficient in the coke oven in advance, and provide a basis for subsequent adjustment measures. If the prediction result shows that the air excess coefficient will exceed the set range, adjustment measures can be taken in time to maintain the stability of the combustion. The accuracy of the prediction directly affects the optimization effect of the whole system, so the implementation of this step can effectively prevent the combustion instability problem and reduce the exhaust emission.

[0042] Step S4: When the predicted air excess coefficient sequence is not within the corresponding reference range, the furnace body is divided into multiple regions according to the real-time thermal stress distribution in the furnace body, the distance of each region from the air inlet is calculated, the air supply component corresponding to each region is calculated, and the air supply rate is regulated according to the distance and the air supply component corresponding to each region;

[0043] Specifically, when the prediction result in step S3 shows that the air excess coefficient is not within the set reference range, the temperature sensor in the coke oven is used to collect thermal stress distribution data of different regions. Based on these temperature data, the coke oven body can be divided into multiple regions, and the temperature and gas quantity in each region are different, so different air supply quantities are needed to ensure the stability of combustion; in each region, the distance of the region from the air inlet is calculated, and the air supply component required by the region is determined according to the temperature and gas quantity of the region. According to this information, the air supply rate can be adjusted, and the region far from the inlet needs to increase the air flow due to the delay of oxygen arrival; while the region close to the inlet can appropriately reduce the air flow; the above technical solution can realize the balanced distribution of air supply in the coke oven through this regional regulation, and avoid the phenomenon of incomplete or excessive local combustion. Adjusting the air flow rate ensures the combustion efficiency of each region, and further optimizes the combustion process of the coke oven, improves the production quality, and reduces the exhaust emission.

[0044] Step S5: Reacquire and judge whether the air supply meets the requirements according to the thermal stress distribution and the air excess coefficient sequence data, and obtain the corresponding adjustment strategy when the requirements are not met.

[0045] Specifically, in this step, the adjusted air supply is evaluated to see if it has achieved the expected target by re-collecting the thermal stress distribution and air excess factor sequence data of the furnace body. According to the changes in the thermal stress distribution and air excess factor within the furnace, it is determined whether there is a temperature unevenness or air excess factor abnormality. If the temperature unevenness is large or the air excess factor still does not meet the set reference range, further adjustment is needed; if the judgment result shows that the air supply still does not meet the requirements, the system will automatically generate corresponding adjustment strategies according to the temperature distribution and air excess factor abnormality. These strategies include adjusting the air supply amount of each region or fine-tuning the air flow rate according to the deviation of the actual temperature of each region from the optimal temperature; the above technical solution avoids the problems of low combustion efficiency or uneven temperature. Through the double feedback mechanism (temperature uniformity and air excess factor), the air supply can be continuously optimized to realize dynamic adjustment of the combustion process. This real-time adjustment mechanism ensures that the coke oven is always in the best working state, thereby improving the production quality and reducing resource waste.

[0046] Further, the air excess factor sequence data changing over time is obtained by collecting the coke oven equipment data through sensors, including: collecting the furnace body temperature distribution data, air supply rate data and coal gas flow fluctuation data in real time through the sensors of each equipment of the coke oven; calculating the air excess factor sequence data changing over time based on the furnace body temperature distribution data, air supply rate data and coal gas flow fluctuation data; performing data cleaning and outlier removal on the sequence data to obtain the air excess factor sequence data.

[0047] Specifically, the above-mentioned coke oven each device is installed with temperature sensor, flow meter and rate sensor, wherein the temperature sensor is distributed in different parts of the oven body, records the oven body temperature distribution data; the air supply rate data is obtained by the rate sensor installed in the air inlet pipeline, measures the air volume per unit time; the coal gas flow fluctuation data is captured by the flow meter, records the instantaneous change of coal gas input. And the above-mentioned oven body temperature distribution data, air supply rate data and coal gas flow fluctuation data are taken as the above-mentioned oven body equipment data, this real-time collection ensures the data capture the dynamic change in the combustion process of the coke oven, which is beneficial to the accuracy of subsequent calculation; the above-mentioned air excess coefficient is calculated by the formula a0=(actual air quantity / theoretical air quantity), wherein the actual air quantity is the product of the air supply rate data and the unit time, and the theoretical air quantity depends on the coal gas composition, for example: the oxygen required for the combustion of CO and H2 in coal gas is different, and because the main source of oxygen is air, the theoretical air quantity is calculated according to the product of the above-mentioned coal gas composition ratio, the air quantity proportion corresponding to each coal gas composition and the unit time coal gas flow. The actual air quantity and the theoretical air quantity correspond to the same unit time, for example: 5s, for each unit time, an air excess coefficient value is generated according to these data, these values form a sequence that changes with time, the data at each position in the above-mentioned sequence contains a timestamp and the calculated air excess coefficient value. Because the temperature is different at different positions of the coke oven, that is, the above-mentioned temperature gradient data can reveal the temperature difference of different parts of the coke oven, larger temperature difference may mean that there is larger heat loss in some areas of the coke oven, for example, the temperature of the wall or the roof is lower. The low temperature in these areas may lead to lower local combustion efficiency, so more air supply is needed to maintain the stability of combustion. Therefore, the average temperature in the above-mentioned coke oven needs to be obtained according to the above-mentioned temperature gradient data to optimize the above-mentioned air excess coefficient, for example: a1=k*a0, k is the optimization coefficient, which can be obtained by actual test, and a1 is taken as the optimized air excess coefficient. In order to ensure data quality, data cleaning is carried out to eliminate outliers and fill in missing data to generate the above-mentioned excess coefficient sequence data. The above-mentioned technical scheme can provide accurate monitoring and adjustment basis for the combustion process of the coke oven by obtaining the sequence data of the air excess coefficient changing with time.

[0048] Further, a periodic model of the air excess coefficient is determined, comprising:

[0049] The air excess coefficient sequence data is subjected to frequency domain transformation to extract periodic fluctuation characteristics; the periodic change law of the oxygen concentration in the combustion chamber is analyzed according to the periodic fluctuation characteristics; the main fluctuation frequency and amplitude characteristics are determined according to the periodic change law; and the periodic model of the oxygen concentration in the combustion chamber changing with time is generated through the main fluctuation frequency and amplitude characteristics.

[0050] Specifically, the air excess coefficient sequence data is subjected to frequency domain transformation to obtain frequency domain transformation data. The frequency domain transformation algorithm can be fast Fourier transform. Periodic fluctuation characteristics in the sequence are identified by analyzing the frequency domain transformation data. The periodic fluctuation characteristics include periodically changing fluctuation patterns and corresponding fluctuation periods. The periodic variation law of the oxygen concentration in the combustion chamber, i.e., the air excess coefficient, is analyzed, and the main fluctuation frequency and amplitude characteristics are determined to accurately identify the period of oxygen concentration variation. The correlation between the periodic fluctuation characteristics and the historical data of the oxygen concentration in the combustion chamber is analyzed to identify the repeated patterns of the oxygen concentration over time. The main fluctuation frequency, corresponding amplitude characteristics, and corresponding pattern identifier of each repeated pattern are trained based on a machine learning algorithm to obtain the periodic model. The periodic model can be used to predict the variation trend of the air excess coefficient under the corresponding repeated pattern, thereby laying a foundation for adjusting the air supply speed according to the repeated pattern.

[0051] Further, according to the periodic model, the coke oven operating parameters are adjusted to obtain combustion stability data, including:

[0052] According to the frequency domain transformation result corresponding to the obtained real-time air excess coefficient sequence data, the periodic model is inputted to obtain a periodic pattern, and a predicted air excess coefficient sequence is obtained based on the reference air excess coefficient sequence data corresponding to the periodic pattern and the real-time air excess coefficient sequence data.

[0053] Based on the predicted air excess sequence and the gas data, the air supply amount sequence in the future time period is calculated.

[0054] Specifically, the real-time air surplus coefficient sequence data obtained through the above steps is input into the periodic model after being converted into a frequency domain, and the periodic pattern corresponding to the frequency domain conversion result is obtained. The similarity between the real-time air surplus coefficient sequence data and the reference air surplus coefficient sequence corresponding to the periodic pattern is compared, the position of the real-time air surplus coefficient sequence data in the reference air surplus coefficient sequence is obtained, and the change trend of the real-time air surplus coefficient in the future time period, i.e., the future air surplus coefficient sequence, is obtained. The coal gas data includes coal gas composition, proportion, and coal gas flow data. The theoretical air quantity of each coal gas composition is calculated according to the proportion, flow data of each coal gas composition, and the corresponding relationship between each coal gas composition and air quantity. The sum of the theoretical air quantity corresponding to each coal gas composition is taken as the theoretical air total quantity. The product of each data in the future air surplus coefficient sequence and the corresponding theoretical air total quantity is taken as the air supply quantity sequence. Through the technical solution, the air supply quantity sequence in the future time period can be obtained, which lays a foundation for further regulating the air flow rate according to the air supply quantity sequence.

[0055] Further, the air supply rate is adjusted according to the air supply quantity sequence and the thermal stress distribution, including:

[0056] The thermal stress distribution in the furnace body is obtained in real time by the temperature sensor arranged at different positions of the furnace body, the region is divided according to the thermal stress distribution, the air supply component of each region is calculated according to the temperature, coal gas quantity, and corresponding air quantity ratio of each region, the distance between each region and the air inlet is calculated, each region is sorted according to the distance from large to small, and the air flow rate is adjusted based on the sorting, the air supply component corresponding to each region, and the distance. The farther the distance corresponding to the region is, the larger the air flow rate corresponding to the region is, and vice versa. When the distance is greater than a preset distance, the actual air supply component is increased by a preset proportion of the corresponding air supply component.

[0057] Specifically, in the coke production process, due to the different temperatures of different parts of the coke oven, the relatively low temperature part will cause the local combustion efficiency to be low, so more air supply is needed to maintain the stability of combustion, on the contrary, the part with high temperature needs relatively less air supply, in order to prevent the air distribution at the position far away from the air inlet and with low temperature from being less when the air flows into the above-mentioned coke oven at a constant flow rate through the air inlet, so that the production quality of coke in the whole coke oven is not balanced, therefore, in order to make the air supply of each area in the above-mentioned coke oven meet the demand, the thermal stress distribution in the above-mentioned oven body is obtained in real time by the temperature sensors arranged at different positions in the oven body, that is, the image composed of the position information and the corresponding temperature information of the temperature sensor, the above-mentioned oven body space is divided into multiple areas according to the temperature distribution, that is, multiple areas uniformly distributed with the position of the above-mentioned temperature sensor as the center, wherein the above-mentioned temperature sensor is uniformly distributed, the gas quantity is obtained based on the volume of each area, and the air supply quantity corresponding to each area is calculated according to the relationship between the ratio of the gas quantity to the air quantity required for combustion at the temperature, and the distance between each area and the air inlet is also calculated, wherein the closer the distance, the more the oxygen in the air can be fully utilized by the gas in the corresponding area, thereby improving the combustion efficiency, on the contrary, the combustion efficiency is lower, therefore, in order to make the gas in each area be able to be fully combusted, the above-mentioned areas are sorted according to the distance from the above-mentioned air inlet from large to small, and the air flow rate of each of the above-mentioned areas is adjusted according to the above-mentioned sorting and the air supply quantity corresponding to each area, wherein the farther the distance, the greater the corresponding air flow rate, on the contrary, the smaller the corresponding air flow rate, and since the areas far away from the air inlet will cause oxygen loss through other areas during the air reaching process, therefore, when the distance is greater than a preset distance, the actual air supply quantity is increased by a preset proportion according to the corresponding air supply quantity, the above-mentioned technical solution can make each area in the above-mentioned coke oven be able to obtain sufficient air supply, thereby ensuring the balance of the coke production quality in the coke oven.

[0058] Further, judging whether the air supply meets the requirement comprises:

[0059] After the air supply rate is adjusted, the thermal stress distribution and the air excess sequence data are re-acquired, and the temperature uniformity is obtained according to the difference between the maximum temperature value and the minimum temperature value in the thermal stress distribution, when the temperature uniformity and the air excess coefficient sequence data are both within the corresponding set range, the air supply meets the requirement, on the contrary, it does not meet the requirement.

[0060] Specifically, after the air supply rate is adjusted, the air supply amount in each region meets the air amount demand of the region, so that the gas in each region is fully combusted, so that the temperature in different regions is balanced, and the air surplus coefficient should be close to 1. In order to verify this result, the thermal stress distribution in the furnace and the air excess coefficient sequence data are reacquired, and the temperature uniformity is calculated according to the difference between the highest temperature and the lowest temperature in the thermal stress distribution. When the temperature uniformity and the air excess coefficient sequence data are both within the set range, it is determined that the air supply meets the requirements. If either of the two is not within the corresponding set range, the air supply does not meet the requirements. The above technical solution can accurately determine whether the air supply meets the requirements, that is, whether the coke oven can produce high-quality products after the air supply speed and the supply amount of each region are adjusted, which lays a foundation for further optimizing the production conditions in the furnace.

[0061] Further, when the air supply does not meet the requirements, the corresponding adjustment strategy is obtained, including:

[0062] When the air supply does not meet the requirements, when the temperature uniformity is not within the corresponding set range and the air excess coefficient sequence data is within the corresponding range, according to the deviation value of the temperature in each region from the optimal temperature, when the deviation value is greater than a set value, the air supply rate of the region is adjusted according to the temperature of the adjacent region of the region in the air flow direction; when the temperature uniformity is within the corresponding range and the air excess coefficient sequence is not within the corresponding range, according to the deviation of the average temperature from the optimal temperature, the air supply amount in each region is reduced or increased in equal proportion; when the temperature uniformity and the air excess coefficient sequence data are not within the corresponding range, the air supply rate and the previous steps are repeated.

[0063] Specifically, since the requirement is not met, it is divided into multiple cases, wherein when the temperature uniformity is not within the set range and the air excess coefficient is normal, it is indicated that the total air supply amount meets the requirement, but the air supply component between each region and the demand of the corresponding region does not match, so each region is traversed, the deviation value of the actual temperature and the optimal temperature is calculated, and it is judged according to the air flow direction whether the corresponding air supply rate of the region is too large or too small, for example: the temperature of the region corresponding to the rear end of the air flow direction of the region is too high, which is normal before, which indicates that the air supply rate corresponding to the region is too large and should be reduced, and vice versa, the air supply rate should be increased, while ensuring that the air supply component of the region is unchanged, so that the heat distribution is balanced; when the air excess coefficient is out of the set range and the temperature uniformity is normal, it is indicated that each region can accurately reach the specified region through the corresponding air supply rate, but the total air supply amount is not enough to make the air excess coefficient not within the set range, therefore, according to the deviation of the average temperature of the furnace body and the optimal temperature, the air supply components of all regions are increased or decreased in proportion, so as to ensure that the above coke oven is in the best working environment or state, and when the above temperature uniformity and the above air excess coefficient sequence are not within the corresponding range, it is indicated that the above adjustment effect is poor, so the air supply component and the air supply speed of each region are re-adjusted through the above steps S1-S4. The above technical scheme, through the double verification mechanism of temperature uniformity and air excess coefficient, can ensure the production quality of coke and reduce resource waste.

[0064] Further, the temperature sensors in the coke oven are uniformly distributed.

[0065] The application also provides a coke oven air excess coefficient control system based on real-time monitoring, which is used to realize the above method. Figure 2 As shown in the figure, the system comprises:

[0066] An acquisition unit is configured to collect coke oven equipment data and thermal stress distribution through a sensor, and acquire sequence data of air excess coefficient changing with time.

[0067] A determination unit is configured to perform frequency domain analysis on the air excess coefficient sequence data, and determine a periodic model of the air excess coefficient.

[0068] A prediction unit is configured to acquire a predicted air excess coefficient sequence according to the frequency domain data corresponding to the real-time air excess coefficient and the periodic model.

[0069] The control unit is configured to, when the predicted air excess coefficient sequence is not within the corresponding reference range, obtain and divide the furnace into multiple regions according to a real-time thermal stress distribution in the furnace, calculate the distance of each region from the air inlet, calculate the air supply component corresponding to each region, and control the air supply rate according to the distance and the air supply component corresponding to each region; re-obtain and judge whether the air supply meets the requirements according to the thermal stress distribution and the air excess coefficient sequence data, and obtain a corresponding adjustment strategy when the requirements are not met.

[0070] The application further provides a computer readable storage medium, which stores instructions, and the instructions are executed by a processor to implement the method.

[0071] In summary, the core technical solution of the coke oven air excess coefficient control method based on real-time monitoring is to achieve dynamic balance between combustion stability and production quality through multi-step closed-loop cooperative control, to collect furnace temperature distribution data, air supply rate data and coal gas flow fluctuation data in real time through uniformly distributed sensors, to optimize and calculate the air excess coefficient sequence based on the furnace temperature distribution data, to form a high-precision time sequence after data cleaning, to provide reliable input for frequency domain analysis, to extract periodic fluctuation characteristics of the sequence data by using fast Fourier transform (FFT), to determine the main frequency and amplitude characteristics of the oxygen concentration in the combustion chamber, to construct a periodic model to accurately capture the internal law of oxygen concentration change, to input real-time frequency domain data into the periodic model, to match historical periodic patterns and predict future air excess coefficient sequences; when the predicted value exceeds the reference range, the furnace is divided into multiple regions according to the real-time thermal stress distribution, the air supply component is calculated in combination with the temperature, coal gas amount and air amount ratio of each region, and the distance of each region from the air inlet is calculated; the regions are sorted in descending order of distance, the air flow rate is adjusted based on the distance weight, that is, the farther the distance, the greater the flow rate, that is, the oxygen transmission loss is compensated, and when the distance exceeds a preset threshold, the actual supply amount is increased by a preset proportion of the air supply component; the flow rate of the region close to the air inlet is reduced to avoid local over-oxygen; the thermal stress distribution and the air excess coefficient sequence data are re-obtained, the temperature uniformity is calculated, and the two indexes are checked: if the temperature uniformity and the air excess coefficient are within the set range, it is determined that the requirements are met; otherwise, an adjustment strategy is executed, the air supply rate is adjusted according to the deviation of the temperature of each region from the optimal temperature and in combination with the temperature of the adjacent region in the air flow direction, the air supply component is adjusted in proportion to the deviation of the average temperature from the optimal temperature, that is, the optimal temperature parameter matching the high-quality production, and the proportion can be an empirical value; the steps S1-S4 are iteratively optimized until the stable production quality is output, and the method solves the problem that the traditional technology cannot simultaneously optimize the combustion efficiency through frequency domain periodic analysis, thermal stress partition control and flow direction cooperative adjustment.

[0072] The above description is only the preferred embodiment of the present application and the explanation of the technical principles. It should be understood by those skilled in the art that the scope of the protection of the present application is not limited to the technical solutions formed by the specific combinations of the above technical features. It should also cover other technical solutions formed by the combinations of the above technical features or their equivalents without departing from the concept of the present application. For example, the technical solutions formed by replacing the above features with the technical features with similar functions disclosed in the present application (but not limited to) and the like.

Claims

1. A coke oven air excess factor control method based on real-time monitoring, characterized by, The method comprises the following steps: Collecting coke oven equipment data and thermal stress distribution through sensors to obtain sequence data of air excess coefficient changing over time; Performing frequency domain analysis on the sequence data of air excess coefficient to determine the periodic model of air excess coefficient; Obtaining the predicted air excess coefficient sequence according to the frequency domain data corresponding to the real-time air excess coefficient and the periodic model; When the predicted air excess coefficient sequence is not within the corresponding reference range, dividing the oven body into multiple regions according to the real-time thermal stress distribution in the oven body, calculating the distance of each region from the air inlet, calculating the air supply component corresponding to each region, and adjusting the air supply rate according to the distance and the air supply component corresponding to each region; Re-obtaining and dividing the oven body into multiple regions according to the thermal stress distribution and the sequence data of air excess coefficient, and judging whether the air supply meets the requirements, and obtaining the corresponding adjustment strategy when it does not meet the requirements.

2. The method of claim 1, wherein, The obtaining of the sequence data of air excess coefficient changing over time comprises the following steps: Real-time collection of oven temperature distribution data, air supply rate data and gas flow fluctuation data through sensors of each device of the coke oven; based on the oven temperature distribution data, air supply rate data and gas flow fluctuation data, sequence data of air excess coefficient changing over time is calculated; the sequence data is cleaned and outliers are removed to obtain the sequence data of air excess coefficient.

3. The method of claim 1, wherein, The determination of the periodic model of air excess coefficient comprises the following steps: Using fast Fourier transform algorithm to analyze the sequence data of air excess coefficient and extract periodic fluctuation characteristics; According to the periodic fluctuation characteristics, analyze the periodic variation law of the oxygen concentration in the combustion chamber; According to the periodic variation law, determine the main fluctuation frequency and amplitude characteristics; Through the main fluctuation frequency and amplitude characteristics, generate a periodic model of oxygen concentration changing over time in the combustion chamber.

4. The method of claim 1, wherein, The obtaining of the predicted air excess coefficient sequence comprises the following steps: According to the frequency domain transformation result corresponding to the obtained real-time air excess coefficient sequence data, input the periodic model to obtain the periodic mode, and based on the reference air excess coefficient sequence data corresponding to the periodic mode and the real-time air excess coefficient sequence data, obtain the predicted air excess coefficient sequence.

5. The method of claim 1, wherein, When the predicted air excess coefficient sequence is not within the corresponding reference range, the adjustment of the air supply rate comprises the following steps: Real-time acquisition of the thermal stress distribution in the oven body through temperature sensors arranged at different positions of the oven body, division of regions according to the thermal stress distribution, calculation of the air supply component of each region according to the temperature, gas quantity and corresponding air quantity ratio of each region, calculation of the distance of each region from the air inlet, sorting of each region according to the distance from large to small, and adjustment of the air flow rate based on the sorting, the air supply component corresponding to each region and the distance, wherein the farther the distance of the region is, the larger the air flow rate corresponding to the region is, and vice versa, and when the distance is greater than a preset distance, the air supply component is increased by a preset proportion as the actual air supply component.

6. The method of claim 1, wherein, The judgment of whether the air supply meets the requirements comprises the following steps: After the air supply rate is adjusted, the thermal stress distribution and the air excess coefficient sequence data are re-acquired, and a temperature uniformity is acquired according to a difference between a maximum temperature value and a minimum temperature value in the thermal stress distribution; when both the temperature uniformity and the air excess coefficient sequence data are within corresponding set ranges, the air supply meets the requirement; otherwise, the air supply does not meet the requirement.

7. The method of claim 6, wherein, When the air supply does not meet the requirement, a corresponding adjustment strategy is acquired, including: When the air supply does not meet the requirement, when the temperature uniformity is not within a corresponding set range and the air excess coefficient sequence data is within a corresponding range, according to a deviation value of a temperature in each region from an optimal temperature, when the deviation value is greater than a set value, an air supply rate of the region is adjusted according to a temperature of a neighboring region of the region in an air flow direction; when the temperature uniformity is within a corresponding range and the air excess coefficient sequence data is not within a corresponding range, according to a deviation of the average temperature from the optimal temperature, air supply components in each region are proportionally reduced or increased; when neither the temperature uniformity nor the air excess coefficient sequence data is within a corresponding range, the air supply rate and the previous steps are repeated.

8. The method of claim 1, wherein, The temperature sensors in the coke oven are uniformly distributed.

9. A coke oven air excess factor control system based on real-time monitoring for implementing the method according to any one of claims 1 to 8, characterized in that, The system comprises: An acquisition unit is configured to acquire coke oven equipment data and a thermal stress distribution through sensors, and acquire sequence data of an air excess coefficient changing over time; A determination unit is configured to perform frequency domain analysis on the air excess coefficient sequence data, and determine a periodic model of the air excess coefficient; A prediction unit is configured to acquire a predicted air excess coefficient sequence according to frequency domain data corresponding to a real-time air excess coefficient and the periodic model; A regulation unit is configured to, when the predicted air excess coefficient sequence is not within a corresponding reference range, acquire and divide a furnace body into multiple regions according to a real-time thermal stress distribution in the furnace body, calculate a distance of each region from an air inlet, calculate an air supply component corresponding to each region, regulate an air supply rate according to the distance and the air supply component corresponding to each region, re-acquire and determine whether the air supply meets the requirement according to the thermal stress distribution and the air excess coefficient sequence data, and acquire a corresponding adjustment strategy when the air supply does not meet the requirement.

10. A computer-readable storage medium having stored thereon instructions, the instructions comprising, The instructions are executed by the processor to implement the method according to any one of claims 1-8. The instructions are executed by the processor to implement the method according to any one of claims 1-8.

Images